Fall 2015 Syllabus is below table

Did you receive a registration error for Fall 2015? Send me an email with the following answers:
1. What registration error did you get (copy/paste is best)?
2. What is your UNM ID?
3. What is your Math/Stat background (that is, do you have the pre-reqs)?

If you are waitlisted, as long as there are seats available I will override you into the course. Don’t worry.

Before first day:

Step 0: Set up LaTeX, R, and Rstudio

Before our first class (Tue 8/18) please read through the following and install the required software on your computer. If you don’t have a computer, campus resources are available (but we’ll need to make special arrangements). This video will guide you though the installations.

R + knitr + LaTeX is required (Note: this is what happens in my class if you use MS Word)

News: (I reserve the right to continue to improve the materials throughout the semester.)
Updating for Fall 2015 — will be a “flipped” class. Lectures will become reading before class with a brief quiz, and class periods will be spent working on assignments and projects, often in teams. (Instead of having you struggle alone at home with homework.)

Timetable

(content will be filled in soon for F15)Each week has this structure:

Pre-class (Tuesday): Reading, Video, Quiz (due before class)

In-class: Activities in class Tuesday and Thursday

Post-class (Thursday): Homework (crowdgrader, due following Thursday before class)

Syllabus

Description: A detailed examination of essential statistical computing skills needed for research and industrial work. Students will use R to develop algorithms for solving a variety of statistical problems using resampling and simulation techniques such as the bootstrap, Monte Carlo methods, and Markov chain methods for approximating probability distributions. Applications to linear and non-linear models will be stressed with emphasis on reproducible research, efficient data manipulation, and visualization throughout.Prerequisite: Stat 528 (ADA2)Semesters offered: Fall or SpringLecture: Stat 590.001 (CRN 53402), TR 14:00–15:15, SMLC 120 VideoOffice hours: Tue 12:30-13:30, Thu 12:30-13:30, and by appointment in SMLC 312email: “Erik B. Erhardt” <erike@stat.unm.edu>, please include “SC1” in subject lineTextbook: Peter Dalgaard, “Introductory Statistics with R“, Second Edition, 2008, ISBN: 978-0-387-79053-4. The book is not required, but it will provide a backup for what you learn in class. Many other books are available with similar material.Laptops running R: I encourage you to bring a laptop to class each day so you can try the R programming exercises in class. If you don’t have one, no problem, teamwork is encouraged — sit next to someone friendly who likes to share.

Teaching Assistants and Peer Mentors

None.

Student learning outcomes

General outcomes:

Organize knowledge in graphs, tables, and code to support concise, comprehensible, and scientifically defensible written interpretations to produce knowledge within a reproducible research environment (R + knitr + LaTeX).

Evaluate and verify code, and assess/criticize for improvements in correctness or efficiency.

Topical outcomes:

Use statistical software, such as R, to read and manage data, create informative plots, report numerical summaries, and apply statistical models, by recommended programming practice including abstraction and documentation.

Apply various types of apply functions to split, apply, and combine for efficient and effective data processing.

Apply concepts of data visualization to improve visual communication and to critique figures for improvements.

Use MCMC methods for statistical estimation, to improve estimates in standard situations and to make estimation possible in unusual situations.

Implement good optimization strategies for a range of scenarios.

Evaluate and criticize published studies, the work of peers, and your own work and assess what was done well, what could be done better, and examine whether their conclusions are supported using statistical principles.

Meeting the learning outcomes

You will acquire new information in this class, but the emphasis is comprehending, integrating, and applying information. Rote factual memorization is the lowest form of learning. Effective learning takes place by explaining, integrating, applying, and analyzing facts, hypotheses, and theories.

Learning in this class occurs by:

Doing – completion of exercises that require analysis of data to answer questions and test hypotheses, or researching answers to reading assignments.

Discussion – interaction with classmates to assemble and synthesize information you’d utilizing the collective skills and knowledge base of the group.

Listening, acting, and reflecting – activities during class time provide insights into information not available in readings and includes review difficult material to aid comprehension. Note taking permits later reflection on lecture content. Listening to the professor lecture is the least effective learning tool for both students, however, and you should plan on coming to every class prepared to participate in active and reflective learning opportunities.

Assessment

Quizzes will be due each Tuesday before class. Purpose: to assess reading and video comprehension and assure you’re prepared to actively participate in class activities with minimal lecture. (About 12, 15% of final grade.) Most weeks plan for 1-2 hours reading and video, 20 minute quiz.

Participation is required in every class. If you’re engaged with the material and your classmates, you’ll get full points. If you’re not in class, working on other things, etc., then you’re not meeting my expectations. Purpose: to struggle and find success in class with the concepts and skills. (10% of final grade.)

Homework (HW) assignments are assigned each Thursday and due the following Thursday, submitted to crowdgrader (75% of HW grade). Purpose: to apply concepts and skills to your class poster project. (About 12, 75% of final grade.) Most weeks plan on 3-5 hours per assignment.

Peer grading is due by the following Tuesday after each homework is due (25% of HW grade). Purpose: to gain skill assessing the work of others, as well as see alternative strategies to answer questions. Most weeks this will take about 30 minutes to grade 3 other students’s HW.

Final grade may include a small buffer at the discretion of the instructor. For example, final grade could be the total points earned divided by the total possible points times 0.95 for graduate students and 0.90 for undergraduate students. That is [Final Grade] = [Points Earned]/[Points possible * 0.95], so that your grade is slightly higher than you earned.

All assignments in this class are electronic, submitted to crowdgrader.com for grading.

Rubrics guide assessment (and self-assessment) of homework, code, projects, exams, and presentations. Each assignment will have its own specific rubric.

All R code for the assignment should be included with the part of the problem it addresses (for code and output use a fixed-width font, such as Courier).
Do NOT use your R code and output as your answer to the problem, but include them to show me how you arrived at your answer. Your prose solution (in a non-fixed-width font) should be provided in addition to R output.

Collaboration and citation

For homeworks I encourage you to work together. Please discuss the data, code, and problems with one another, but do your own exploration and write up. We expect everyone to hand in substantially different homeworks, and we will enforce this under the honor code. The small benefit you might get from plagiarism is not worth the severe penalty (of lost trust, being reported to the dean, no points for the assignment, etc.).

As in life, please use any resources available to you. Projects and some homeworks will explicitly encourage you to use resources on the internet, but showing extra initiative will always be appreciated. You may find R programming tough at first, so feel free discuss your problems with other classmates or meet with or email questions to the TAs or me.

I encourage you to use the ideas of others, but make them your own, giving credit. For projects have a formal bibliography, for homework cite casually, and for code simply copy the URL in as a comment (which is doubly helpful for finding the resource again).

Disability statement

If you have a documented disability that will impact your work in this class, please contact me to discuss your needs. You’ll also need to register with the Accessibility Resource Center in 2021 Mesa Vista Hall (building 56) across the courtyard east from the SUB.

Learning without thought is labor lost.
What I hear, I forget.
What I see, I remember.
What I do, I understand.
– Confucius

Random stuff

UNM has license for free online access to the definitive books for the Lattice and ggplot2 graphing platforms. Note you must be on campus or logged in through the UNM proxy to access these.

R is currently available in these UNM Locations: DSH 141 and 143, Econ 1004, SMLC pods, and SUB IT-LoboLab Pod and IT-LoboLab Classroom.

Asking smart questions
“Smart Questions” guide (note “hackers build things, crackers break them”)
Email Question Rubric:
* Send one email per question.
— Use “Reply” to continue conversation on a question; send a new email for a new question.
* Include “Stat590” as the first word of the subject line in new emails (if replying, just use reply).
* Begin email with a short question summary.
* When possible, include commented code in email body
— Comments should indicate where the problem is, what the expected behavior is, and what steps are necessary to reproduce problem.
— Code should include a “Minimum representative test cast” (http://www.catb.org/esr/faqs/smart-questions.html#code)
* If attaching code, please include all the files necessary to run your code (data, etc.).

Acumen in Statistics

Erik Barry Erhardt, PhD, is an Associate Professor of Statistics at the University of New Mexico Department of Mathematics and Statistics, where he has served as Director of the Statistics Consulting Clinic, and is currently Director of the Biostatistics and NeuroInformatics (BNI) Core for the second phase of the Center for Biomedical Research Excellence (COBRE) in Brain Function and Mental Illness at the Mind Research Network. His research interests include Bayesian and Frequentist statistical methods for stable isotope sourcing and brain imaging. Erik is a Howard Hughes Medical Institute Interfaces Scholar collaborating in interdisciplinary research and offering consulting services in statistics.